The term “steganography” generally means data hiding. One form of data hiding is digital watermarking. Digital watermarking is a process for modifying media content to embed a machine-readable (or machine-detectable) signal or code into the media content. For the purposes of this application, the data may be modified such that the embedded code or signal is imperceptible or nearly imperceptible to a human, yet may be detected through an automated, machine-based detection process. Most commonly, digital watermarking is applied to media content such as images, audio signals, and video signals. Digital watermarks can be incorporated into images or graphics that are then printed, e.g., on product packaging.
Digital watermarking systems may include two primary components: an embedding component that embeds a watermark in media content, and a reading component that detects and reads an embedded watermark (referred to as a “watermark reader,” or “watermark decoder,” or simply as a “reader” or “decoder”). The embedding component (or “embedder” or “encoder”) may embed a watermark by altering data samples representing the media content in the spatial, temporal or some other domain (e.g., Fourier, Discrete Cosine or Wavelet transform domains). The reading component (or “reader” or “decoder”) may analyze target content to detect whether a watermark is present. In applications where the watermark encodes information (e.g., a message or auxiliary information), the reader may extract this information from a detected watermark.
A watermark embedding process may convert a message, signal, etc., into a payload conveyed by a watermark signal. The embedding process may then combine the watermark signal with media content and possibly other signals (e.g., a transform domain-based orientation pattern or synchronization signal) to create watermarked media content. The process of combining the watermark signal with the media content may be a linear or non-linear function. The watermark signal may be applied by modulating or altering signal samples in a spatial, temporal or transform domain.
A watermark encoder may analyze and selectively adjust media content to give it attributes that correspond to the desired message symbol or symbols to be encoded. There are many signal attributes that may encode a message symbol, such as a positive or negative polarity of signal samples or a set of samples, a given parity (odd or even), a given difference value or polarity of the difference between signal samples (e.g., a difference between selected spatial intensity values or transform coefficients), a given distance value between watermarks, a given phase or phase offset between different watermark components, a modulation of the phase of a host signal associated with the media content, a modulation of frequency coefficients of the host signal, a given frequency pattern, a given quantizer (e.g., in Quantization Index Modulation) etc.
The present assignee's work in steganography, data hiding and digital watermarking is reflected, e.g., in U.S. Pat. Nos. 7,013,021, 6,993,154, 6,947,571, 6,912,295, 6,891,959, 6,763,123, 6,718,046, 6,614,914, 6,590,996, 6,449,377, 6,408,082, 6,345,104, 6,122,403 and 5,862,260. Some 3rd-party work is reflected in, e.g., U.S. Pat. Nos. 7,130,442; 6,208,735; 6,175,627; 5,949,885; 5,859,920. Each of the patent documents identified in this paragraph is hereby incorporated by reference herein in its entirety. Of course, a great many other approaches are familiar to those skilled in the art, e.g., Avcibas, et al., “Steganalysis of Watermarking Techniques Using Images Quality Metrics”, Proceedings of SPIE, January 2001, vol. 4314, pp. 523-531; Dautzenberg, “Watermarking Images,” Department of Microelectronics and Electrical Engineering, Trinity College Dublin, 47 pages, October 1994; Hernandez et al., “Statistical Analysis of Watermarking Schemes for Copyright Protection of Images,” Proceedings of the IEEE, vol. 87, No. 7, July 1999; J. Fridrich and J. Kodovský. Rich models for steganalysis of digital images, IEEE Transactions on Information Forensics and Security, 7(3):868-882, June 2011; J. Kodovský, J. Fridrich, and V. Holub. Ensemble classifiers for steganalysis of digital media, IEEE Transactions on Information Forensics and Security, 7(2):432-444, 2012; and T. Pevný, P. Bas, and J. Fridrich. Steganalysis by subtractive pixel adjacency matrix, IEEE Transactions on Information Forensics and Security, 5(2):215-224, June 2010; I. J. Cox, M. L. Miller, J. A. Bloom, J. Fridrich, and T. Kalker. Digital Watermarking and Steganography, Morgan Kaufman Publishers Inc., San Francisco, Calif., 2007; R. O. Duda, P. E. Hart, and D. H. Stork. Pattern Classification. Wiley Interscience, New York, 2nd edition, 2000; each of which is hereby incorporated herein by reference in its entirety. The artisan is presumed to be familiar with a full range of literature concerning steganography, data hiding and digital watermarking.
Digital watermarking may be used to embed auxiliary information into cover media (e.g., images, packaging, graphics, etc.) such that changes to the cover media to convey the digital watermarking remain invisible to humans but allows machines to reliably extract the auxiliary information even after common signal-processing operations (e.g., noise, filtering, blurring, optical capture). This allows machines to uniquely identify objects depicted in captured imagery. Digital watermarking has been used for applications including media content protection, track and trace, etc.